Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system for flow-induced noise source identification, comprising: a data processing system for noise source identification that processes data representing a fluid low in volume representing a physical space, the data processing system comprising: one or more processing devices and one or more hardware storage devices storing instructions that are operable, when executed by the one or more processing devices, to cause the one or more processing devices to perform operations comprising: simulating activity of a fluid in a volume that represents a physical space, the activity of the fluid in the volume being simulated so as to model movement of elements within the volume; identifying a first set of vortices in a transient and turbulent flow modeled by the fluid flow that occurs at a first time in the fluid flow simulation; identifying a second set of vortices in the transient and turbulent flow that occurs at a second subsequent time in the fluid flow simulation; tracking changes in the vortices by comparing the first set of discrete vortices and the second set of discrete vortices; identifying a plurality of noise sources based on the tracking; determining the contribution of the plurality of noise sources, which occurs at a receiver location, at effecting noise at the receiver location that is at a predetermined location within the volume; and outputting data that is rendered on a display device indicating distribution of the noise sources throughout the volume and to the receiver location.
This invention relates to a system for identifying and analyzing flow-induced noise sources in fluid dynamics simulations. The system addresses the challenge of pinpointing and quantifying noise generated by turbulent and transient fluid flows, which is critical in fields such as aerodynamics, hydrodynamics, and mechanical engineering where noise reduction is a priority. The system includes a data processing system that simulates fluid flow within a defined volume representing a physical space. The simulation models the movement of fluid elements, capturing transient and turbulent flow behavior. The system identifies vortices at two distinct time points—an initial time and a subsequent time—and tracks changes in these vortices over time. By comparing the vortex sets, the system identifies noise sources and determines their contributions to noise at a specified receiver location within the volume. The results are visualized on a display, showing the distribution of noise sources and their impact on the receiver. This approach enables engineers to analyze how fluid dynamics generate noise, helping optimize designs for reduced noise in applications such as aircraft, vehicles, and industrial machinery. The system provides actionable insights by correlating vortex dynamics with noise generation, facilitating targeted noise mitigation strategies.
2. The system of claim 1 , wherein determining the contribution comprises: applying a transfer function to each of the plurality of noise sources, with the transfer function determining the contribution based on a relationship between a location of the respective noise source and the predetermined location of the receiver.
This invention relates to noise source localization and contribution analysis in acoustic systems. The problem addressed is accurately determining the contribution of multiple noise sources to the overall noise experienced at a specific receiver location. Traditional methods often fail to account for spatial relationships between sources and receivers, leading to inaccurate noise assessments. The system includes a receiver positioned at a predetermined location and a plurality of noise sources with known or measurable locations. The system determines the contribution of each noise source to the noise at the receiver by applying a transfer function. This transfer function calculates the contribution based on the spatial relationship between each noise source's location and the receiver's location. The transfer function may incorporate factors such as distance, angle, or environmental conditions to model how noise propagates from each source to the receiver. By analyzing these spatial relationships, the system provides a more accurate assessment of each noise source's impact on the receiver. This approach improves noise mitigation strategies by identifying the most significant contributors to the noise at the receiver's location. The system can be applied in various environments, including industrial settings, urban planning, or acoustic testing, where precise noise source identification is critical.
3. The system of claim 2 , wherein the transfer function applied to each of the plurality of noise sources is a frequency dependent transfer function.
This invention relates to noise reduction systems, specifically for managing multiple noise sources in an environment. The problem addressed is the difficulty in accurately modeling and mitigating noise from various sources, particularly when their acoustic properties vary with frequency. Traditional systems often apply uniform noise reduction techniques, which may not effectively address frequency-dependent noise characteristics. The system includes a noise reduction module that processes signals from multiple noise sources. Each noise source is analyzed using a frequency-dependent transfer function, which adjusts the noise reduction parameters based on the specific frequency components of the noise. This allows the system to dynamically adapt to different noise profiles, improving overall noise suppression performance. The transfer function accounts for variations in noise frequency content, ensuring that high-frequency and low-frequency noise are treated appropriately. The system may also include a signal processing unit that receives input signals from sensors or microphones, identifies noise sources, and applies the frequency-dependent transfer function to each. The noise reduction module then generates an output signal with reduced noise, preserving the desired audio content. This approach enhances clarity in audio applications such as communication devices, hearing aids, or environmental noise control systems. The frequency-dependent transfer function ensures that noise reduction is optimized across the entire frequency spectrum, addressing the limitations of conventional methods that use fixed or uniform noise reduction techniques.
4. The system of claim 1 , further comprising: combining at least some of the plurality of noise sources into one or more clusters of noise sources that are clustered based, at least in part, on the contribution of each of the noise sources.
This invention relates to noise source clustering in signal processing systems. The problem addressed is the need to efficiently identify and group noise sources in a system to improve signal analysis, noise reduction, or other applications where noise characterization is important. The system includes a noise source identification module that detects and isolates multiple noise sources in a signal. These noise sources are then analyzed to determine their individual contributions to the overall noise profile. The system further includes a clustering module that groups at least some of the identified noise sources into one or more clusters. The clustering is based, at least in part, on the contribution of each noise source, meaning that sources with similar or related contributions are grouped together. This clustering helps in simplifying noise analysis, reducing computational complexity, or enabling targeted noise mitigation strategies. The system may also include a noise source tracking module to monitor changes in noise sources over time, ensuring that the clustering remains accurate as conditions evolve. The overall goal is to provide a more organized and manageable way to handle noise sources in various applications, such as audio processing, environmental monitoring, or industrial systems.
5. The system of claim 4 , further comprising: comparing a strength of each of the one or more noise source contributions to a threshold value for inclusion in the cluster.
This invention relates to noise source identification and clustering in audio processing systems. The problem addressed is accurately identifying and grouping multiple noise sources in an environment to improve noise reduction or source separation. The system captures audio signals from one or more microphones and processes them to identify distinct noise sources. Each noise source is analyzed to determine its contribution to the overall noise field, with contributions being compared to a threshold value. Only contributions exceeding this threshold are included in a cluster representing a distinct noise source. This clustering helps distinguish between relevant noise sources and background noise, enabling more effective noise suppression or source-specific processing. The system may also include steps for spatial filtering, directional analysis, or frequency-domain processing to enhance noise source identification accuracy. The threshold comparison ensures that only significant noise contributions are clustered, reducing false positives and improving computational efficiency. This approach is useful in applications like speech enhancement, acoustic monitoring, and environmental noise analysis.
6. The system of claim 4 , wherein combining the plurality of the one or more noise sources into the one or more clusters improves the processing performance of the system.
This invention relates to a noise processing system designed to enhance performance by clustering multiple noise sources. The system identifies and groups similar noise sources into clusters to streamline processing. By organizing noise sources into these clusters, the system reduces computational overhead and improves efficiency. The clustering process involves analyzing characteristics of each noise source, such as frequency, amplitude, or temporal patterns, to determine similarities. Once clustered, the system processes the grouped noise sources collectively rather than individually, which minimizes redundant calculations and optimizes resource utilization. This approach is particularly useful in applications where real-time noise reduction or signal enhancement is required, such as audio processing, environmental monitoring, or communication systems. The clustering mechanism ensures that the system can handle a large number of noise sources without degrading performance, making it scalable and adaptable to various environments. The overall goal is to improve processing speed and accuracy by leveraging the structured organization of noise sources.
7. The system of claim 6 , further comprising building a physical object based using the physical modifications.
This invention relates to a system for creating physical objects by applying physical modifications to a base material. The system addresses the challenge of efficiently transforming digital designs into tangible objects by automating the process of applying precise physical modifications to a material substrate. The system includes a processing unit that generates instructions for modifying the material, a modification apparatus that applies the physical changes to the material according to the instructions, and a control unit that coordinates the operations between the processing unit and the modification apparatus. The physical modifications may include cutting, shaping, or altering the material properties to produce a desired object. The system further includes a feedback mechanism that monitors the modification process to ensure accuracy and adjusts the instructions as needed. The invention also involves building a physical object based on the applied modifications, ensuring the final product matches the intended design specifications. This system is particularly useful in manufacturing, prototyping, and custom fabrication where precise material transformation is required. The automated approach reduces human error and improves production efficiency.
8. The system of claim 1 , wherein the receiver is located at a position corresponding to an audio recording device or a human ear.
The system relates to audio signal processing, specifically improving audio capture by optimizing receiver placement. The problem addressed is the degradation of audio quality due to suboptimal receiver positioning, which can lead to poor sound localization, interference, or signal distortion. The system includes a receiver positioned at a specific location to enhance audio fidelity. This receiver is strategically placed to correspond either to an audio recording device, such as a microphone, or to a human ear. By aligning the receiver with these natural or device-based sound capture points, the system ensures accurate sound reproduction and minimizes phase or amplitude discrepancies. The receiver may incorporate directional or omnidirectional capabilities to further refine audio capture based on environmental conditions. The system may also include signal processing components to filter or amplify received audio signals, ensuring high-quality output. This approach is particularly useful in applications like teleconferencing, virtual reality, or hearing aids, where precise audio localization is critical. The invention improves upon prior art by dynamically adjusting receiver placement to match the intended listening or recording position, thereby enhancing overall audio performance.
9. The system of claim 1 , further comprising applying a transfer function to the one or more sound generating flow regions subsequent to the combining.
The invention relates to a system for generating sound using fluid dynamics, specifically by manipulating flow regions within a fluid medium to produce audible sound waves. The system addresses the challenge of creating sound without traditional mechanical transducers, offering a novel approach for applications where conventional speakers are impractical or inefficient. The system includes a fluid medium through which one or more sound generating flow regions are created. These flow regions are manipulated to produce sound waves by altering their properties, such as velocity, pressure, or turbulence. The system combines these flow regions to enhance sound generation, ensuring coherent and controlled acoustic output. Additionally, a transfer function is applied to the combined flow regions to refine the sound characteristics, such as frequency, amplitude, or waveform, improving the quality and precision of the generated sound. The transfer function may involve mathematical operations, signal processing, or feedback mechanisms to adjust the flow regions dynamically. This allows the system to produce complex sound patterns, including music or speech, with high fidelity. The invention is particularly useful in environments where traditional speakers are unsuitable, such as underwater, in high-temperature or high-pressure conditions, or in applications requiring minimal mechanical components. By leveraging fluid dynamics, the system provides a robust and versatile alternative to conventional sound generation methods.
10. The system of claim 9 , further comprising comparing a strength of each of the one or more noise source to a threshold value and excluding at least one noise source that has a strength beneath the threshold.
This invention relates to noise source detection and filtering in signal processing systems. The problem addressed is the presence of weak or irrelevant noise sources that can degrade system performance by introducing unnecessary data or computational overhead. The system detects and analyzes multiple noise sources in an environment, evaluates their signal strengths, and selectively filters out those below a predefined threshold to improve accuracy and efficiency. The system includes a noise source detection module that identifies one or more noise sources in a monitored environment. Each detected noise source is analyzed to determine its signal strength. A comparison module then evaluates the strength of each noise source against a threshold value. If a noise source's strength falls below the threshold, it is excluded from further processing. This filtering step ensures that only significant noise sources are considered, reducing computational load and improving system reliability. The invention is particularly useful in applications where noise source identification is critical, such as environmental monitoring, acoustic signal processing, or wireless communication systems. By dynamically filtering weak noise sources, the system enhances signal clarity and operational efficiency. The threshold value can be adjusted based on application requirements, allowing flexibility in balancing sensitivity and performance.
11. The system of claim 1 , wherein the plurality of noise sources is a first set of noise sources, the system further comprising: determining a second set of one or more noise sources based on a second simulation that corresponds to different physical conditions in or around the physical space.
This invention relates to a system for analyzing noise sources in a physical space, particularly for optimizing noise reduction or management. The system identifies and evaluates multiple noise sources within or around a physical space using simulations that model different physical conditions. The system includes a first set of noise sources determined from a first simulation representing specific physical conditions. Additionally, it determines a second set of noise sources based on a second simulation that corresponds to different physical conditions, allowing for a comparative analysis of noise sources under varying scenarios. This enables the system to assess how noise sources change under different environmental or operational conditions, supporting better decision-making for noise mitigation strategies. The system may also include components for generating the simulations, processing the simulation data, and visualizing the results to highlight key noise sources and their variations across conditions. The invention is useful in applications such as urban planning, industrial noise control, and environmental monitoring, where understanding noise behavior under different conditions is critical.
12. The system of claim 1 , further comprising causing a physical modification to one or more physical objects based on the identified area for design change.
This invention relates to a system for modifying physical objects based on identified design changes. The system operates in the domain of automated design optimization and physical manufacturing, addressing the problem of efficiently implementing design modifications to physical objects without manual intervention. The system identifies areas within a design that require changes, such as structural weaknesses or inefficiencies, and then applies those changes to one or more physical objects. The physical modification may involve altering the object's shape, material composition, or other physical properties to improve performance, durability, or functionality. The system integrates design analysis with physical manufacturing processes, enabling automated adjustments to objects based on real-time or pre-determined design criteria. This approach reduces the need for manual intervention, speeds up the design-to-production cycle, and ensures consistency in applying modifications across multiple objects. The system may be used in industries such as aerospace, automotive, or consumer electronics, where precise and repeatable modifications are critical. The invention enhances efficiency in manufacturing by automating the transition from digital design changes to physical object adjustments.
13. The system of claim 12 , further comprising optimizing a design change to minimize the contribution of one or more noise sources.
Technical Summary: This invention relates to systems for optimizing design changes in engineering applications to reduce noise contributions. The system is designed to address the problem of unwanted noise in mechanical, electrical, or structural designs, which can degrade performance, efficiency, or user experience. Noise sources may include vibrations, electromagnetic interference, or acoustic disturbances, and minimizing their impact is critical in industries such as automotive, aerospace, and consumer electronics. The system includes a computational model that analyzes the effects of design modifications on noise propagation. It evaluates how changes to material properties, geometric configurations, or operational parameters influence noise generation and transmission. The optimization process involves simulating multiple design iterations to identify configurations that minimize noise contributions from one or more identified sources. This may include adjusting damping characteristics, altering structural resonances, or implementing shielding techniques. The system may also incorporate machine learning or statistical methods to predict noise behavior and guide the optimization process. By iteratively refining the design, the system ensures that the final configuration meets performance targets while reducing noise-related issues. This approach is particularly useful in applications where noise reduction is a critical requirement, such as in quiet environments or high-precision systems. The invention provides a structured method for systematically improving designs to achieve optimal noise performance.
14. A computer-implemented method of simulating activity of a fluid in a volume that represents a physical space, the activity of the fluid in the volume being simulated so as to model movement of elements within the volume, the method comprising: identifying a first set of vortices in a transient and turbulent flow modeled by the fluid flow that occurs at a first time in the fluid flow simulation; identifying a second set of vortices in the transient and turbulent flow that occurs at a second subsequent time in the fluid flow simulation; tracking changes in the vortices by comparing the first set of discrete vortices and the second set of discrete vortices; identifying one or more noise sources based on the tracking; determining the contribution that occurs at a receiver location of the one or more noise sources at effecting noise at the receiver location that is at a predetermined location within the volume; and outputting data that is rendered on a display device indicating distribution of the noise sources throughout the volume and to the receiver location.
This invention relates to fluid dynamics simulation, specifically modeling transient and turbulent fluid flows to analyze noise generation and propagation. The method simulates fluid activity within a defined volume representing a physical space, tracking the movement of fluid elements to model complex flow behaviors. The process involves identifying discrete vortices at two different time points in the simulation—first at an initial time and then at a subsequent time—to capture the evolution of turbulent structures. By comparing these vortex sets, the system tracks changes in vortex behavior, which are then analyzed to identify noise sources within the fluid flow. The method calculates the noise contribution from these sources to a predetermined receiver location within the volume, quantifying how each source affects noise levels at that point. The results are visualized, showing the spatial distribution of noise sources and their impact on the receiver. This approach enables detailed acoustic analysis in fluid dynamics simulations, useful for applications like aerodynamics, hydrodynamics, and environmental noise modeling.
15. The computer-implemented method of claim 14 , wherein determining the contribution comprises: applying a transfer function to at least one noise source, with the transfer function determining the contribution based on a relationship between a location of the noise source and a location of the receiver.
This invention relates to noise source localization and contribution analysis in acoustic environments. The method involves determining the contribution of at least one noise source to a received sound signal at a receiver location. The contribution is calculated by applying a transfer function to the noise source, where the transfer function accounts for the spatial relationship between the noise source and the receiver. This relationship considers factors such as distance, orientation, and environmental conditions that affect sound propagation. The method may involve multiple noise sources, each processed individually or collectively to assess their combined impact on the receiver. The transfer function can be derived from empirical data, simulation models, or real-time measurements to accurately represent how sound travels from the source to the receiver. This approach enables precise identification of dominant noise sources and their relative contributions, which is useful in applications like noise reduction, acoustic monitoring, and environmental sound analysis. The method may also integrate with other noise mitigation techniques to enhance accuracy and effectiveness.
16. The computer-implemented method of claim 15 , wherein the transfer function applied to each of the plurality of noise sources is a frequency dependent transfer function.
This invention relates to noise reduction in audio processing systems, specifically addressing the challenge of accurately modeling and mitigating multiple noise sources with varying frequency characteristics. The method involves analyzing a plurality of noise sources in an audio signal, where each noise source is processed using a distinct transfer function. These transfer functions are frequency-dependent, meaning they adjust the noise reduction based on the frequency content of each noise source. This allows for more precise noise suppression tailored to the spectral properties of different noise sources. The method first identifies and separates the noise sources within the audio signal, then applies the corresponding frequency-dependent transfer functions to each source. This approach improves noise reduction performance by accounting for the unique frequency behavior of each noise source, leading to clearer audio output. The technique is particularly useful in environments with complex noise profiles, such as speech enhancement in noisy conditions or audio signal processing in communication devices. By dynamically adapting the transfer functions to the frequency characteristics of the noise, the method achieves more effective and targeted noise suppression compared to traditional methods that use uniform noise reduction across all frequencies.
17. The computer-implemented method of claim 14 , further comprising: combining at least some of the one or more noise sources into one or more clusters of noise sources that are clustered based, at least in part, on the contribution of the one or more noise sources.
This invention relates to noise source analysis in computing systems, specifically improving the identification and clustering of noise sources to enhance system performance. The method involves detecting and analyzing multiple noise sources within a computing environment, where noise sources are defined as any factors that disrupt system operations, such as background processes, network traffic, or hardware interference. The method evaluates the contribution of each noise source to system performance degradation, measuring factors like processing overhead, latency, or resource consumption. Based on these contributions, the method clusters similar noise sources into groups, allowing for more efficient management and mitigation. The clustering process ensures that noise sources with comparable impacts are treated collectively, reducing redundancy in mitigation strategies. This approach optimizes system performance by prioritizing the most significant noise sources and applying targeted solutions. The method may also include visualizing the clustered noise sources to assist in troubleshooting and decision-making. By dynamically adjusting noise source clustering based on real-time performance data, the system adapts to changing conditions, ensuring sustained efficiency. This technique is particularly useful in high-performance computing, cloud environments, and real-time systems where noise mitigation is critical.
18. The computer-implemented method of claim 17 , further comprising: comparing a strength of each of the one or more noise source contributions to a threshold value for inclusion in the cluster.
This invention relates to noise source identification and clustering in audio processing systems. The problem addressed is accurately detecting and grouping multiple noise sources in an environment to improve noise reduction or source separation. The method involves analyzing audio signals to identify distinct noise sources and their contributions, then clustering these sources based on their characteristics. A key aspect is comparing the strength of each noise source contribution to a threshold value to determine whether it should be included in a cluster. This ensures that only significant noise sources are grouped, improving the accuracy of noise modeling and reduction. The method may also involve determining the number of noise sources present, estimating their contributions, and refining the clustering process to enhance separation between different noise sources. The technique is useful in applications like speech enhancement, hearing aids, and environmental noise monitoring, where distinguishing between multiple noise sources is critical for effective processing. The threshold comparison step helps filter out weak or irrelevant noise contributions, ensuring that only meaningful sources are considered in the clustering process. This improves the robustness and efficiency of noise source identification in complex acoustic environments.
19. The computer-implemented method of claim 18 , further comprising comparing a strength of each of the one or more noise source to a threshold value and excluding at least one noise source that has a strength beneath the threshold.
This invention relates to noise source identification and filtering in audio processing systems. The problem addressed is the presence of unwanted noise in audio signals, which can degrade signal quality and interfere with speech recognition, communication, or audio analysis applications. The method involves detecting and analyzing multiple noise sources in an audio environment to improve signal clarity. The method first captures an audio signal containing one or more noise sources. Each noise source is identified and its strength is measured. The strength of each noise source is then compared to a predefined threshold value. Noise sources with strengths below the threshold are excluded from further processing, effectively filtering out weak or irrelevant noise. This selective filtering helps reduce computational overhead while maintaining the integrity of stronger, more relevant noise sources for further analysis or suppression. The method may also involve determining the direction of each noise source relative to a reference point, such as a microphone array, to spatially distinguish between different noise sources. By combining strength-based filtering with spatial analysis, the system can more accurately isolate and manage noise sources in complex environments. This approach enhances audio signal processing by dynamically adapting to varying noise conditions.
20. The computer-implemented method of claim 18 , wherein combining the plurality of the one or more noise sources into the one or more clusters improves the processing performance of the computer-implemented method.
This invention relates to computer-implemented methods for processing noise sources in data analysis, particularly improving computational efficiency. The method involves analyzing a dataset containing multiple noise sources, which are signals or data points that interfere with or distort the primary data of interest. The core challenge addressed is the computational overhead associated with processing large volumes of noise sources individually, which can slow down analysis and reduce accuracy. The method clusters the noise sources into one or more groups based on shared characteristics, such as frequency, amplitude, or temporal patterns. By grouping similar noise sources, the system reduces redundancy in processing, allowing for more efficient data handling. This clustering step optimizes computational resources by minimizing repetitive calculations and enabling parallel processing where applicable. The clustered noise sources are then processed collectively rather than individually, which enhances the overall performance of the data analysis system. The improvement in processing performance is achieved by reducing the number of distinct operations required, leveraging shared properties within each cluster to streamline calculations. This approach is particularly useful in applications like signal processing, machine learning, and data filtering, where noise reduction is critical but computationally expensive. The method ensures that the analysis remains accurate while significantly improving speed and resource utilization.
21. The computer-implemented method of claim 14 , wherein the receiver is located at a position corresponding to an audio recording device or a human ear.
This invention relates to audio signal processing, specifically improving the accuracy of audio source localization by optimizing receiver placement. The problem addressed is the difficulty in precisely determining the origin of sound sources in environments where reflections, noise, or obstructions distort audio signals. Traditional methods often fail to account for the physical positioning of receivers, leading to inaccuracies in localization. The invention describes a method for enhancing audio source localization by strategically positioning a receiver at a location that corresponds to either an audio recording device or a human ear. This positioning ensures that the receiver captures audio signals with minimal distortion, improving the accuracy of source identification. The method involves analyzing the acoustic environment to determine optimal receiver placement, which may include adjusting the receiver's position dynamically based on real-time conditions. By aligning the receiver with the natural listening position of a recording device or human ear, the system reduces errors caused by indirect sound paths and environmental interference. The invention also includes techniques for calibrating the receiver's position relative to known reference points, ensuring consistent performance across different environments. This approach is particularly useful in applications such as speech recognition, surveillance, and virtual reality, where precise audio localization is critical. The method may be implemented in software, hardware, or a combination of both, depending on the specific application requirements.
22. The computer-implemented method of claim 14 , further comprising applying a transfer function to the one or more sound generating flow regions subsequent to the combining.
The invention relates to computational fluid dynamics (CFD) and acoustic analysis, specifically addressing the challenge of accurately modeling and predicting sound generation in fluid flow systems. The method involves identifying one or more sound generating flow regions within a fluid domain, where these regions are areas where fluid dynamics produce significant acoustic emissions. These regions are then combined into a unified representation to simplify analysis. A transfer function is subsequently applied to the combined sound generating flow regions to model how sound propagates from these regions to a target location, such as a microphone or observer. The transfer function accounts for factors like distance, medium properties, and geometric obstacles, enabling accurate prediction of sound pressure levels and frequency spectra at the target location. This approach improves the efficiency and accuracy of acoustic simulations in applications like aerodynamics, automotive design, and industrial machinery, where noise reduction is critical. The method leverages computational techniques to bridge the gap between fluid dynamics and acoustics, providing a more integrated and reliable analysis framework.
23. The computer-implemented method of claim 14 , wherein the plurality of noise sources is a first set of noise sources, the method further comprising: determining a second set of one or more noise sources based on a second simulation that corresponds to different physical conditions in or around the physical space.
This invention relates to computer-implemented methods for analyzing noise sources in a physical space, particularly in environments where noise conditions vary due to changing physical conditions. The method involves simulating noise propagation to identify and characterize noise sources under different scenarios. Initially, a first simulation is performed to determine a first set of noise sources in the physical space under a specific set of physical conditions. The method then extends this analysis by conducting a second simulation that corresponds to different physical conditions, such as changes in temperature, humidity, or structural modifications, to identify a second set of noise sources. By comparing the results of these simulations, the method enables a comprehensive understanding of how noise sources evolve under varying environmental or operational conditions. This approach is useful in applications like industrial noise management, urban planning, or acoustic design, where dynamic conditions influence noise propagation and mitigation strategies. The method leverages computational modeling to predict noise behavior, allowing for adaptive solutions that account for real-world variability.
24. The computer-implemented method of claim 14 , further comprising causing a physical modification to one or more physical objects based on the identified area for design change.
This invention relates to computer-implemented methods for modifying physical objects based on identified design changes. The method involves analyzing a digital model of an object to determine an area where a design change is needed. The identified area is then used to generate instructions for modifying one or more physical objects corresponding to the digital model. The method further includes executing these instructions to cause a physical modification to the physical objects, such as through manufacturing, assembly, or other fabrication processes. The system may use machine learning or optimization algorithms to identify the optimal area for modification, ensuring the changes improve the object's performance, durability, or other desired characteristics. The physical modifications may include altering dimensions, material properties, or structural features of the object. This approach automates the transition from digital design to physical implementation, reducing manual intervention and improving efficiency in manufacturing and prototyping workflows. The invention is particularly useful in industries like aerospace, automotive, and consumer electronics, where precise and iterative design modifications are critical.
25. The computer-implemented method of claim 24 , further comprising building a physical object based using the physical modifications.
This invention relates to computer-aided manufacturing and additive fabrication, specifically addressing the challenge of creating physical objects with precise modifications based on digital designs. The method involves generating a digital model of an object, which includes defining a base structure and one or more physical modifications to be applied to that structure. These modifications may include alterations to the object's geometry, material properties, or other physical characteristics. The digital model is then processed to produce instructions for a fabrication device, such as a 3D printer or CNC machine, which constructs the object according to the specified modifications. The method ensures that the final physical object accurately reflects the intended design, including all modifications, by translating digital parameters into physical fabrication steps. This approach improves the precision and customization of manufactured objects, enabling the production of complex, tailored designs with high fidelity to the original digital model. The invention is particularly useful in industries requiring high-precision manufacturing, such as aerospace, medical devices, and advanced prototyping.
26. The computer-implemented method of claim 25 , further comprising optimizing a design change to minimize the contribution of one or more noise sources.
This invention relates to computer-implemented methods for optimizing design changes in systems where noise sources affect performance. The method involves analyzing a system to identify noise sources that contribute to unwanted variations or disturbances in the system's behavior. Once identified, the method optimizes design changes to minimize the impact of these noise sources, improving system reliability and performance. The optimization process may involve adjusting system parameters, modifying components, or implementing control strategies to reduce noise contributions. The method may also include simulating the system under different conditions to evaluate the effectiveness of proposed design changes. By systematically reducing noise sources, the invention aims to enhance the robustness and efficiency of the system. The approach is applicable to various technical domains, including mechanical, electrical, and software systems, where noise mitigation is critical for achieving desired performance outcomes. The optimization process may leverage machine learning or other computational techniques to identify optimal design modifications that minimize noise contributions while maintaining or improving overall system functionality.
27. A computer program product stored on a computer readable non-transitory storage medium the computer program product for flow-induced noise source identification, the computer program product comprising instructions to cause a system to: simulate activity of a fluid in a volume that represents a physical space, the activity of the fluid in the volume being simulated so as to model movement of elements within the volume; identify a first set of vortices in a transient and turbulent flow modeled by the fluid flow that occurs at a first time in the fluid flow simulation; identify a second set of vortices in the transient and turbulent flow that occurs at a second subsequent time in the fluid flow simulation; track changes in the vortices by comparing the first set of discrete vortices and the second set of discrete vortices; identify a plurality of noise sources based on the tracking; determine the contribution of the plurality of noise sources, which occurs at a receiver location, at effecting noise at the receiver location that is at a predetermined location within the volume; and output data that is rendered on a display device indicating distribution of the noise sources throughout the volume and to the receiver location.
This invention relates to computational fluid dynamics (CFD) and noise source identification in transient and turbulent fluid flows. The problem addressed is the difficulty in accurately identifying and quantifying noise sources generated by fluid dynamics, particularly in complex, time-varying flows where vortices contribute to sound generation. The invention provides a computer program product that simulates fluid activity within a defined volume representing a physical space, modeling the movement of fluid elements. The program identifies vortices in the turbulent flow at two distinct time points, then tracks changes between these vortex sets to determine noise sources. It calculates the contribution of these sources to noise at a specified receiver location within the volume. The results are visualized, showing the distribution of noise sources and their impact on the receiver. The solution enables precise localization and quantification of noise-generating vortices in dynamic flows, aiding in the design of quieter systems. The program outputs data that can be rendered on a display, providing insights into noise propagation and source distribution. This approach improves upon traditional methods by dynamically tracking vortex evolution and its acoustic effects over time.
28. The computer program product of claim 27 , wherein the instructions to determine the contribution comprises instructions to: apply a transfer function to each of the plurality of noise sources, with the transfer function determining the contribution based on a relationship between a location of the respective noise source and the predetermined location of the receiver.
This invention relates to noise analysis in computing systems, specifically to determining the contribution of multiple noise sources to a receiver at a predetermined location. The problem addressed is accurately assessing how different noise sources impact a receiver, which is critical in fields like signal processing, wireless communications, and environmental monitoring. The invention provides a method to quantify the contribution of each noise source by applying a transfer function that accounts for the spatial relationship between the noise source and the receiver. The transfer function models how the location of each noise source affects its contribution to the overall noise at the receiver. This approach enables precise noise characterization, which is useful for optimizing system performance, reducing interference, and improving signal integrity. The invention can be implemented in software, hardware, or a combination thereof, and is applicable in various domains where noise mitigation is essential. The transfer function dynamically adjusts based on the relative positions of the noise sources and the receiver, ensuring accurate noise contribution calculations in real-time or simulated environments.
29. The computer program product of claim 27 , wherein the instructions to apply the transfer function applies the transfer function to each of the plurality of noise sources that is a frequency dependent transfer function.
Technical Summary: This invention relates to signal processing, specifically to methods for analyzing and mitigating noise in audio or acoustic systems. The problem addressed is the accurate modeling and reduction of noise from multiple sources, particularly when the noise characteristics vary with frequency. The invention involves a computer program product that processes noise data from multiple sources. A key feature is the application of a frequency-dependent transfer function to each noise source. This transfer function adjusts the noise characteristics based on frequency, allowing for precise modeling of how noise propagates or interacts in different environments. By applying distinct transfer functions to each noise source, the system can account for variations in noise behavior across the frequency spectrum, improving accuracy in noise analysis and reduction. The transfer functions are tailored to the specific properties of each noise source, enabling the system to distinguish between different types of noise and their frequency-dependent effects. This approach enhances the ability to isolate, analyze, and mitigate noise in applications such as audio signal enhancement, acoustic testing, or environmental noise control. The invention ensures that noise reduction techniques are optimized for the unique frequency responses of each source, leading to more effective noise management.
30. The computer program product of claim 27 , further comprises instructions to: combine at least some of the plurality of noise sources into one or more clusters of noise sources that are clustered based, at least in part, on the contribution of each of the noise sources.
This invention relates to noise source clustering in computational systems, specifically for analyzing and managing noise contributions from multiple sources in a data processing environment. The problem addressed is the difficulty in identifying and mitigating noise sources that collectively impact system performance, particularly when individual contributions are small but cumulative effects are significant. The invention provides a computer program product that includes instructions for clustering noise sources based on their contribution levels. The system first identifies a plurality of noise sources within a computational environment, where each noise source may include hardware, software, or environmental factors that generate unwanted signals or interference. The program then evaluates the contribution of each noise source to overall system noise, considering factors such as signal strength, frequency, or impact on processing efficiency. The key innovation is the clustering of noise sources into one or more groups based on their contribution levels. Noise sources with similar or related contributions are grouped together, allowing for more efficient analysis and mitigation strategies. This clustering helps prioritize noise reduction efforts by focusing on the most impactful groups rather than individual sources. The system may also apply additional filtering or weighting to refine the clustering process, ensuring that the most relevant noise sources are accurately grouped. By clustering noise sources based on their contributions, the invention enables more effective noise management, improving system reliability and performance in environments where multiple noise sources are present.
31. The computer program product of claim 30 , further comprises instructions to: compare a strength of each of the one or more noise source contributions to a threshold value for inclusion in the cluster.
This invention relates to noise source identification and clustering in audio processing systems. The technology addresses the challenge of accurately detecting and grouping multiple noise sources in an environment to improve audio signal enhancement, such as in speech recognition or noise cancellation applications. The system analyzes audio signals to identify distinct noise sources and evaluates their contributions to the overall noise profile. It includes a method for clustering noise sources based on their characteristics, such as frequency, amplitude, or temporal patterns. The clustering process groups similar noise sources to simplify noise modeling and reduction. A key feature is the comparison of each noise source's strength against a predefined threshold to determine whether it should be included in a cluster. This ensures that only significant noise contributions are considered, reducing computational overhead and improving accuracy. The threshold may be dynamically adjusted based on environmental conditions or user preferences. The invention also involves generating a noise model from the clustered sources, which can be used to filter or suppress noise in real-time audio processing. This approach enhances speech intelligibility and improves the performance of audio-based applications in noisy environments. The system may be implemented in software, hardware, or a combination of both, and can be integrated into devices such as smartphones, hearing aids, or smart speakers.
32. The computer program product of claim 31 , further comprises instructions to: compare a strength of each of the one or more noise source to a threshold value and excluding at least one noise source that has a strength beneath the threshold.
This invention relates to noise source identification and filtering in audio processing systems. The technology addresses the challenge of accurately detecting and mitigating noise sources in environments where multiple noise sources may be present, some of which may be weak or irrelevant to the primary audio signal. The system analyzes audio data to identify one or more noise sources and evaluates the strength of each detected noise source. By comparing the strength of each noise source to a predefined threshold value, the system filters out noise sources that fall below the threshold, thereby improving the signal-to-noise ratio of the processed audio. This selective exclusion of weak noise sources enhances the accuracy and efficiency of noise reduction processes, particularly in applications such as speech recognition, audio enhancement, and environmental noise monitoring. The invention ensures that only significant noise sources are considered, reducing computational overhead and improving the overall performance of the audio processing system.
33. The computer program product of claim 30 , further comprises instructions to: apply a transfer function to the one or more sound generating flow regions subsequent to the combining.
The invention relates to computational fluid dynamics (CFD) simulations for analyzing sound generation in fluid flows. The problem addressed is the need to accurately model and predict aerodynamic noise sources in fluid flow simulations, particularly in regions where sound is generated by turbulent or vortical flow structures. Traditional CFD methods often lack the resolution or computational efficiency to effectively capture these sound-generating mechanisms. The invention provides a computer program product that enhances CFD simulations by identifying one or more sound-generating flow regions within a fluid domain. These regions are then combined into a unified representation to facilitate noise analysis. The program further applies a transfer function to the combined sound-generating regions to refine the acoustic output. The transfer function may adjust parameters such as frequency, amplitude, or spatial distribution of the sound sources to improve accuracy or computational efficiency. The combined regions and transfer function application enable more precise modeling of aerodynamic noise, which is useful in applications like aircraft design, automotive engineering, and industrial machinery where noise reduction is critical. The invention improves upon prior methods by integrating sound source identification, combination, and post-processing in a streamlined computational framework.
34. The computer program product of claim 27 , wherein the plurality of noise sources is a first set of noise sources, the computer program product, further comprises instructions to: determine a second set of one or more noise sources based on a second simulation that corresponds to different physical conditions in or around the physical space.
This invention relates to computer-aided noise analysis in physical spaces, addressing the challenge of accurately modeling and mitigating noise sources under varying environmental conditions. The system generates a noise simulation for a physical space by analyzing a first set of noise sources, such as machinery, traffic, or HVAC systems, and their interactions with the environment. The simulation accounts for factors like sound propagation, reflections, and absorption to predict noise levels at different locations within the space. The invention further includes generating a second simulation for the same physical space but under different physical conditions, such as changes in temperature, humidity, or structural modifications. This second simulation identifies a second set of noise sources that may differ from the first set due to the altered conditions. By comparing the two simulations, the system can assess how noise patterns change under different scenarios, enabling better design decisions for noise reduction, such as repositioning equipment or adding soundproofing materials. The approach improves the accuracy of noise modeling by dynamically adapting to environmental variations, ensuring more effective noise control strategies.
35. A system comprises: a data processing system comprising: one or more processor devices; and one or more hardware storage devices storing executable computer instructions that when executed by the one or more processing devices to cause the one or more processing devices to: simulate activity of a fluid in a volume that represents a physical space, the activity of the fluid in the volume being simulated so as to model movement of elements within the volume; identify in the volume one or more potential sound generating vortex structures undergoing stretching at a non-uniform rate; track changes in the vortex structures; identifying, by the computer system, one or more noise sources based on the tracking; determine at a receiver location the contribution of the identified one or more noise sources at effecting noise at the receiver location that is at a predetermined location within the volume; and output data that is rendered on a display device indicating a distribution of the noise sources throughout the volume.
This system simulates fluid activity within a defined volume representing a physical space, modeling the movement of elements such as particles or molecules. The simulation identifies potential sound-generating vortex structures within the fluid that are undergoing non-uniform stretching, a phenomenon that can produce noise. The system tracks changes in these vortex structures over time to pinpoint noise sources. It then calculates the contribution of these identified noise sources to the overall noise at a specific receiver location within the volume. The system outputs data that visually represents the distribution of noise sources throughout the volume, allowing for analysis of noise generation and propagation. This approach enables the study of aerodynamic or hydrodynamic noise in applications such as aircraft design, automotive engineering, or industrial fluid dynamics, where understanding and mitigating noise sources is critical. The system provides insights into how fluid dynamics influence noise generation, helping optimize designs to reduce unwanted noise.
36. The system of claim 35 , wherein the system determines the contribution by instructions to: apply a transfer function to each of the plurality of noise sources, with the transfer function determining the contribution based on a relationship between a location of the respective noise source and the predetermined location of the receiver.
This invention relates to noise source localization and contribution analysis in acoustic systems. The problem addressed is accurately determining how individual noise sources contribute to overall noise at a specific receiver location, accounting for spatial relationships between sources and the receiver. The system analyzes multiple noise sources in an environment to quantify their individual contributions to noise at a predetermined receiver location. It applies a transfer function to each noise source, where the transfer function models how the source's noise propagates to the receiver based on their relative positions. This spatial relationship calculation allows the system to weight each source's contribution according to its distance and orientation relative to the receiver. The transfer function may incorporate factors like attenuation, reflection, or diffraction that affect noise propagation between the source and receiver. By processing each source through its respective transfer function, the system generates a quantitative measure of each source's specific contribution to the total noise experienced at the receiver location. This enables targeted noise mitigation strategies by identifying the most significant contributors. The system may be used in applications like urban planning, industrial noise control, or acoustic system design where understanding source contributions is critical for effective noise management.
37. The system of claim 36 , wherein the transfer function applied to each of the plurality of noise sources is a frequency dependent transfer function.
This invention relates to noise reduction systems, specifically for managing multiple noise sources in a controlled environment. The system addresses the challenge of accurately modeling and mitigating noise from various sources, which can interfere with desired signals or operations. The system includes a plurality of noise sources, each with an associated transfer function that modifies the noise characteristics. The transfer function is frequency-dependent, meaning it adjusts the noise based on different frequency components, allowing for more precise control and reduction of unwanted noise. This frequency-dependent approach enables the system to target specific frequency ranges where noise is most problematic, improving overall noise suppression performance. The system may also include sensors to detect noise and processing units to apply the transfer functions dynamically, ensuring real-time adaptation to changing noise conditions. By using frequency-dependent transfer functions, the system can achieve more effective noise reduction compared to traditional methods that apply uniform adjustments across all frequencies. This technology is particularly useful in applications requiring high-precision noise control, such as audio processing, communication systems, or industrial environments where noise interference must be minimized.
38. The system of claim 35 , further comprising: combine at least some of the plurality of noise sources into one or more clusters of noise sources that are clustered based, at least in part, on the contribution of each of the noise sources.
This invention relates to noise source clustering in signal processing systems, particularly for identifying and grouping noise sources based on their contributions to overall noise levels. The system analyzes multiple noise sources in an environment, such as industrial machinery, urban soundscapes, or electronic devices, to determine their individual contributions to noise. The system then clusters these noise sources into one or more groups based on their relative contributions, allowing for targeted noise reduction or mitigation strategies. The clustering process may involve statistical analysis, machine learning, or signal processing techniques to group noise sources with similar characteristics or impact levels. This approach helps prioritize noise reduction efforts by identifying the most significant sources, improving efficiency in noise management systems. The system may also integrate with existing noise monitoring or suppression technologies to enhance their effectiveness. By dynamically clustering noise sources, the system adapts to changing environmental conditions, ensuring continuous optimization of noise control measures.
39. The system of claim 38 , further comprising instructions to: compare a strength of each of the one or more noise source contributions to a threshold value for inclusion in the cluster.
This invention relates to noise source identification and clustering in acoustic signal processing. The system analyzes audio data to detect and categorize noise sources, improving signal clarity in environments with multiple overlapping sounds. The problem addressed is the difficulty in accurately isolating and grouping noise sources when multiple sources contribute to the overall acoustic signal, leading to degraded audio quality in applications like speech recognition, noise cancellation, and environmental monitoring. The system processes audio input to identify individual noise sources and their contributions to the overall signal. It then clusters these sources based on their characteristics, such as frequency, amplitude, and temporal patterns. A key feature is the ability to compare the strength of each noise source contribution against a predefined threshold to determine whether it should be included in a cluster. This ensures that only significant noise sources are grouped, reducing computational overhead and improving clustering accuracy. The threshold comparison step helps filter out weak or irrelevant noise contributions, enhancing the system's ability to distinguish between meaningful and background noise. The system may also include additional features such as adaptive threshold adjustment, real-time noise tracking, and dynamic clustering to handle varying acoustic environments. The overall goal is to improve noise source separation and classification for applications requiring high-fidelity audio analysis.
Unknown
August 20, 2019
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.